A recent survey from Evans Data reveals artificial intelligence is hitting its stride, as developers welcome a parade of AI-centric technologies for apps. IDN speaks Evans Data vice president David Intersimone.

"The combination of AI and Big Data is allowing companies to create a new generation of smart [apps] and services."

In 2017, artificial intelligence is hitting its stride with a growing number of AI offerings now available for developers from the biggest names in the sector -- Amazon, Google, HPE, IBM and Microsoft. With so many options, it’s no surprise that developers are trying out a variety of AI-centric technologies for many new applications.

At the end of 2016, 42% of developers said they were using some forms of cognitive computing or artificial intelligence in their development projects--up dramatically from the number of practitioners at the beginning of the year.

Of developers working with some form of AI, more than one-third (34%) are already involved with ‘deep learning’ techniques.

Top adoption is in financial related industries. “While it’s easy to imagine implementations for Deep Learning across many industries, Fin-tech is particularly ripe for this,” said Evans Data CEO Janel Garvin. “AI can help a financial advisor or stock broker make better recommendations that are tailored specifically to a client’s circumstances and needs, but getting AI into the financial services sector can mean a revolutionary breakthrough for banking institutions of all types.”

The Internet of Things (IoT) sector, another data-intensive area, also leverages AI (14.9%).

To get more insight into developer adoption of AI, IDN spoke with Evans Data’s David Intersimone, vice president of developer communities. We got his takes on many trends that explain the run-up to today’s growing focus on AI by developers – and how the coming years will see it become even more mainstream.

Tracing the Connections Between Big Data, Real-Time and AI To begin with, the current spike in developer interest in AI and deep learning is an outgrowth of IT’s multi-year investments in big data and related technologies that aim to capture and analyze huge volumes of data, Intersimone said.

“As we’ve see the rise of Big Data in recent years, we have also seen the rise of new systems and technologies for analyzing and acting on the information in these big data storage systems,” he told IDN. “Enterprises, services, sensors and devices are generating orders of magnitude more data. Companies, in the past, used traditional business intelligence and reporting tools to analyze and display the data in human consumable formats. Humans would pour through the reports and make business decisions.”

That said, there’s another equation at work that Evans Data found key to creating an environment for AI-driven development, Intersimone added. Today’s sky-high data volumes, combined with the need for more rapid (even real-time) results, are stretching traditional BI approaches beyond limits.

“Coinciding with the growth of big data, we’ve seen the creation of new computing architectures (massively parallel systems like IBM Watson), and high performance computing chips (GPUs from Nvidia, Intel and others) that are allowing new machine learning and deep learning systems to be used to aid in recognizing and acting upon big data in real time,” he said. “With more data, it was becoming harder and harder for humans alone to digest and create actionable strategies based on the data.”

Unicorns Are Helping Democratize AI for Developers Experiments and innovations from leading software vendors has led to a flood of novel open source projects – which in turn have made such technologies more widely available. The result, Intersimone said, is a new-gen array of tools are infrastructures now in the hands of innovative developers across many commercial companies.

“Google, Amazon, IBM, Microsoft, Salesforce, Facebook, SAP, Samsung, Nvidia, Intel and others are pioneering new machine learning systems, frameworks and SDKs that allow data scientists and application developers to create a wide range of solutions. These solutions are based on open standards and in many cases open source,” he noted.

The profusion of such AI-deep learning options, many which don’t require developers to give up the languages or tools they love most, further opens the door to experimentation. “The combination of AI and Big Data is allowing companies to create new generation smart personal assistants, intelligent trading systems, autonomous vehicles, computer aided medical diagnostic tools, improved cybersecurity services, improved customer service systems, voice recognition and response solutions, decision support and logistics systems, and more,” Intersimone added.

Building a Team for AI Success That said, making the most of AI-enabled apps “still takes an integrated team of business, data and software engineers to integrate the storage, machine learning, automation and web, mobile and PC based solutions,” he added.

We asked Intersimone to define the players that he would use to comprise an “integrated team” for delivering big ROI from AI-big data projects.

“For larger enterprise solutions you’ll need to involve a wider range of team members across the business, engineering, operations, financial, and sales organizations. In a smaller company or a startup, team members make be responsible for multiple roles,” he advised. “In today’s agile development world with the importance of build solutions and then rapidly updating and delivering the software into a wide range of target platforms and devices, you will see a wider set of team members.”

For team leaders a bit intimidated about building such teams, Intersimone noted many of the creators of these AI tools and approaches are putting together strong programs to promote this new kind of teamwork of developers, IT, and even non-technical business stakeholders.

As an example, he noted one recent IBM developerWorks event where IBM presented what he called ‘new tools for the citizen analyst, business analyst, data engineer, data scientist, application developer and chief data officer’ “One of the demos was for an outdoor retail company that was experiencing sales declines in some of its products. By connecting disparate data sources (structured and unstructured), using machine learning and coordinating the companies disparate team members, the retail company could create applications that helped a customer purchase the right gear for an upcoming trip,” Intersimone said.

Moving Your Career in an AI Direction Beyond the wide range of tools, frameworks, and SDKs available for developers, there are also a growing number of courses available to help stakeholders learn more about employing Big Data, Machine Learning, Deep Learning and AI algorithms technologies. As an example, in addition to such courses from major vendors and the Apache Software Foundation, courses are also available online at Udacity, Coursera and other services.

Given Evans Data suggests that this AI-big data sector is set to pay big dividends for companies – and individual careers – we asked Intersimone for his advice on how traditional app developers and data architects can get started.

“First and foremost, artificial intelligence in all its forms will continue to be the hottest topic in software development and will go from a curiosity to ubiquity as more and more developers incorporate AI libraries and functions into their apps,” he told IDN.

“Developers should become acquainted with the tools, libraries and frameworks for AI and [machine learning] and then consider how they apply to the types of projects they are currently working on. Soon virtually all competitive applications will incorporate some elements of AI and developers need to embrace these new capabilities within the context of their own development focus,” Intersimone said.